Differences in Gut Microbiota Profiles and Microbiota Steroid Hormone Biosynthesis in Men with and Without Prostate Cancer

Take Home Message Gut microbiota of the prostate cancer patients is altered significantly compared with that of benign individuals. Microbial 5-α-reductase, copper absorption, and retinol metabolism are potential mechanisms of action. These findings support the previously observed association of lifestyle, geography, and prostate cancer incidence.


Introduction
Prostate cancer (PCa), despite its high incidence, has undiscovered details of etiology and pathogenesis [1,2].PCa is known to be highly heritable, but lifestyle, socioeconomic, and environmental factors may also affect PCa incidence [3].Diet is one of the most widely studied lifestyle factors, and various nutrients and food products have been reported to be associated with an altered PCa risk [3,4].
PCa incidence differs markedly between geographical locations, being the lowest in Asia and the highest in the Western lifestyle countries [2].Differences in ethnicity, genetics, as well as healthcare-related factors such as intensity of prostate-specific antigen (PSA) screening have a significant effect on the reported variability of global PCa incidence [3].However, other explanatory factors might also exist.It is of great interest that a high rate of incidental PCa has also been reported widely in the geographical areas of a low clinical PCa prevalence [5].Additionally, studies conducted in immigrant populations suggest that nongenetic, individual lifestyle factors may affect PCa risk significantly [6].Based on these observations, one might expect that prostate carcinogenesis affects a significant portion of aging men, if not all of them.One might also expect that individual lifestyle and environmental factors may either stimulate or inhibit the neoplastic process in the prostate, accounting for the geographical differences observed in clinical PCa.However, the mechanisms mediating how lifestyle affects PCa risk remains unclear.
Gut microbiota (GM), that is, a collection of all microbes in the gastrointestinal tract, is considered to affect many metabolic pathways and pathogenetic processes in the human body [7].Furthermore, gut dysbiosis (disequilibrium of the microbiota) leading to low-grade inflammation has been linked to many cancers, also in organs distant from the intestines [7].Chronic inflammation, production of superoxide radicals, growth factors, and bacterial genotoxins have all been proposed as mechanisms of action [7].Furthermore, the alterations in GM could result from the lifestyle factors related to the altered PCa risk.
A majority of the PCa investigations covering microbiological aspects have studied either prostate tissue or urinary tract microbiota with conflicting results [8].To date, the effect of GM on prostate carcinogenesis is documented poorly, although there is some evidence of an association with PCa [8,9].The association of fecal microbiota with PCa has received little attention, and most studies have very small sample sizes [8].Larger studies to date reported differences in GM composition between PCa and non-PCa cases, as well as between high-risk and benign low-risk PCa [9][10][11].Microbiome analyses also suggested mechanisms of action, including altered folate and arginine metabolism, as well as short-chain fatty acids and IGF-1 signaling [9,10,12].
To assess the fecal microbiota profiles of PCa patients compared with their benign counterparts, we conducted a substudy within a prospective clinical trial (NCT02241122) where microbiological swab samples from men with suspected PCa were 16S sequenced, sequences were analyzed with bioinformatic methods, and steroid hormone levels were measured from plasma.To our knowledge, this is the largest and most detailed clinical trial studying the GM of PCa patients.

Trial design
The study cohort has previously been reported in detail [13].

Assessments
After MRI, 12-core systematic and two targeted transrectal biopsies from up to two lesions suspected at MRI (Likert score 3-5) were collected from all participants.Prophylactic antibiotics were given according to the institutional guidelines and have previously been reported in detail [14].In practice, patients received either single or two doses of antibiotics.The distribution of received specific antibiotics was 60% ciprofloxacin, 30% levofloxacin, and 2% fosfomycin.
No enema was administered prior to biopsy.Immediately prior to transrectal biopsies, microbiome samples were collected utilizing sterile rectal swabs (Copan FLOQSwab; Copan Diagnostics Inc., Murrieta, CA, USA) and immediately stored at -20 °C.Blood samples were collected for plasma steroid assays.In addition to clinical specimens, a detailed questionnaire including a family history of PCa, and general medical, travel, and smoking histories was completed prior to biopsy.

Microbiological sample preparation for 16S RNA sequencing
Stool samples were diluted in 1 ml of sterile saline after which 500 ll was used for DNA extraction with the semiautomatic GXT Stool Extraction Kit VER 2.0 and GenoXtract unit (Hain Lifescience GmbH, Nehren, Germany).DNA concentrations were measured with the Qubit dsDNA HS Assay Kit and Qubit 2.0 fluorometer (Life Technologies, Carlsbad, CA, USA).Extracted DNA was stored at -80 °C until use.Bacterial V4 gene regions of 16S rRNA were sequenced in three batches with MiSeq (Illumina, San Diego, CA, USA), including aqua as negative control, and seven ATCC strain 16S rRNA genes in plasmids (Bifidobacterium adolescentis, Escherichia coli, Enterococcus faecalis, Faecalibacterium praus-nitzii, Lactobacillus acidophilus, Staphylococcus epidermidis, and Streptococcus pyogenes).This in-house method has previously been described in detail [15].Microbial analyses were performed with CLC Genomics Workbench Microbial Genomics module v. 12 (QIAGEN Digital Insights, Aarhus, Denmark).Sequences were assigned to operational taxonomic units (OTUs) according to the similarity of sequences with CLC Microbial Genomics module workflow.Quality and ambiguous trims were performed with default settings, with the minimum number of nucleotides set to 150.SILVA 16S v132 97% was used as the reference database [17,18].
Microbial diversity was defined with a-diversity that measures diversity within one sample, and b-diversity that measures similarity or dissimilarity of two distinct samples.
The a-diversity indices Chao1 and Shannon were calculated to evaluate community richness, diversity, and evenness at 10527-rarefaction level.A b-diversity measure, Bray-Curtis, was calculated to assess compositional dissimilarity of two samples between PCa and benign, the statistical significance of which was evaluated with the permutational multivariate analysis of variance (PERMANOVA) test with 99 999 permutations.The sequencing batch effect was controlled with visualization of b-diversity measures (Bray-Curtis and UniFrac) and PERMANOVA tests.
A differential abundance analysis with a generalized linear model that applies a negative binomial distribution was also performed.The sequencing batch and the previously mentioned factors potentially impacting GM were corrected in the analysis.Age was not considered in the analysis because of collinearity with PCa status.The results were filtered with combined abundance in all samples of >100 and a prevalence of !10%.The p values were corrected using the false discovery rate (FDR) approach [19].The statistical significance limit was set at p < 0.05.
A predictive analysis of functional bacterial genes was constructed utilizing Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) v.1.0tool [20].PICRUSt was performed with Qiime v. 1.9 to create Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs and pathways according to the counts of OTUs with the reference data of Greengenes v.13.8 [21][22][23].As microbiota and predicted gene products were not normally distributed, data were analyzed with nonparametric Wilcoxon rank sum and Kruskal-Wallis tests, and described with medians and 95% CIs.

2.6.
Plasma steroid assay The steroid analyses of plasma samples were conducted using liquid chromatography-tandem mass spectrometry according to an established method [24].The measured steroids included androstenedione, dehydroepiandrosterone, dihydrotestosterone (DHT), estradiol, estrone, progesterone, 17-a-hydroxyprogesterone, and testosterone (T).In addition, the DHT/T ratio was calculated for statistical analyses.Wilcoxon rank sum tests and Spearman correlations were used to investigate the association of PICRUStpredicted microbial steroid hormone biosynthesis with plasma steroid status.For Wilcoxon rank sum tests, steroid hormone biosynthesis was divided into high and low according to the median, to compare hypothesized low/normal and high/pathological values.

Study participants
A total of 364 men entered the trial (see the flowchart in Fig. 1).After excluding patients withdrawing consent (n = 24), having MRI artifacts at the time of biopsy (n = 2), having stool material in rectal swabs insufficient for analyses (n = 149), and being in the technically unsuccessful DNA extraction batch (n = 8; Supplementary Fig. 1), a total of 181 men were included in the analyses, of whom 167 provided complete questionnaires.Plasma samples were available from 169 cases, of which four were excluded due to the unknown status of 5-a-reductase inhibitor (5-ARI) medication.Plasma steroids of 5-ARI users were analyzed separately (n = 17, 10%).The basic clinicopathological characteristics and the most significant confounding factors potentially affecting microbiota composition are presented in Table 1.Age, PSA, PSA density, and prostate volume were significantly associated with cancer status and ISUP cancer grade.PCa was diagnosed in 60% and clinically significant PCa in 46% of cases (respectively, 108/181 and 207/338, 61% PCa/43% clinically significant PCa for the whole cohort).Of the 108 diagnosed PCa cases, 23% were ISUP grade 1, 45% ISUP grade 2-3, and 31% ISUP grade 4-5.Potentially confounding factors were similar between groups except that the PCa group contained fewer active smokers (7%) than the benign group (13%).Smoking was not associated with cancer grade (Table 1).

Gut microbial diversity
According to the a-diversity indices Chao1 (p = 0.7) and Shannon (p = 0.3), no significant differences were found in bacterial community richness or evenness (Supplementary Fig. 2-4).On the contrary, compositional dissimilarity differed between PCa cases and men without PCa (PERMA-NOVA p = 0.039; Fig. 2A), but these differences were not reflected in the cancer grade (PERMANOVA p = 0.23; Supplementary Fig. 5).Principal coordinate 1 in the Bray-Curtis analysis appears to consist of mostly Prevotella 9 (10%; Fig. 2B), which was the most abundant single genus in the GM and associated with PCa in the differential abundance analysis.

GM composition and abundance
The 181 successfully sequenced samples contained bacterial taxa from 21 different phyla.At the family level, OTU clustering showed Prevotellaceae becoming gradually more abundant with increasing cancer grade, but the elevation was not statistically significant in the differential abundance analysis (max group mean 144 000, ISUP grade 4-5 vs benign Log2 fold change 0.61, p = 0.86; Fig. 3).Furthermore, differential abundance analysis showed that Alloprevotella, Prevotella 2, and Prevotella 9 within the family Prevotellaceae were more abundant in the cancer group.
Other significantly abundant genera belonged to Acidaminococcaceae, Christensenellaceae, Clostridiales vadinBB60, Corynebacteriaceae, Enterobacteriaceae, Erysipelotrichaceae, Lachnospiraceae, Muribaculaceae, Ruminococcaceae, Synergistaceae, and Veillonellaceae families.Genera higher and lower in PCa are presented in Figure 4 (Supplementary Table 1).The microbial taxa, which had significantly different abundance in the cancer and benign groups, had variable abundance correlation with the different PCa ISUP grades.For example, some microbes were especially abundant in low-grade cancers (eg, Acidaminococcus), while others were higher in men with higher-grade cancers (eg, UBA1819, [Clostridium] innocuum; Supplementary Fig. 6A-C).

Discussion
This study evaluated potential links between GM and PCa using GM profiles of 181 patients with suspected PCa.The 108 patients in whom PCa was diagnosed had significant differences in their GM signatures compared with the remaining 73 patients.Predictive functional analyses suggest that differences in the steroid hormone, copper, and retinol pathways could be the metabolic consequences of the altered microbiota.The hormone dependency of PCa could render it susceptible to alterations in steroid hormones caused by GM, which were also studied.Men having a predicted alteration in gut steroid hormone metabolism, measured as predicted higher microbial 5-AR, also had a pattern of plasma steroid concentration that differed from those not having this predicted elevated level.The detailed differential abundance analysis revealed several alterations in the abundant and minor members of GM.The most abundant genus of the whole cohort, Prevotella 9, was also elevated in PCa.The family Prevotellaceae has been shown to be affected especially by diet since it is able to degrade complex plant polysaccharides, but different genera of the family may have distinct functions in promoting disease or supporting health [25].Another abundant member, Escherichia-Shigella from the Enterobacteriaceae family, includes the opportunistic pathogen E. coli that has colibactin-mediated genotoxic properties and has been reported in PCa patients previously [7,10].By contrast, members of the Erysipelotrichaceae family have been sug-   gested to play a role in the pathogenesis of several diseases, such as IBDs and metabolic disorders, despite being usually a minor member of the GM community [26].Similarly to our results, Christensenellaceae was elevated and Moryella had a negative correlation with high-risk PCa in a previous report [11].To conclude, several microbes higher in PCa have been linked to PCa as well as other diseases, highlighting the potential disease promoting properties of GM.How- It is of great interest that the PICRUSt analysis indicated elevated steroid hormone biosynthesis pathways in the GM of the cancer cases, since it is well known that 5-AR reduces T to DHT feeding PCa.Formerly, higher steroid hormone biosynthesis has been discovered in the microbiota of patients with androgen deprivation therapy (n = 9) than in men without therapy (n = 16) [27].Furthermore, the contri-bution of GM to the rate of PCa tumor growth and progression of castration resistance through steroid hormones in animal models as well as in human samples were shown recently [28].These studies suggest that steroid metabolism of GM is associated with therapy response.However, our study suggests that microbiota-enriched 5-AR activity may have more profound effects in prostate carcinogenesis.In addition, we demonstrated that the systemic steroid hormone levels were associated with altered predicted microbial 5-AR.Moreover, we have recently shown high concentrations of DHT in the gut of mice models as well as in human individuals, with GM being responsible for the metabolic activity [29].The potential mechanistic explanation for the altered steroid hormone metabolism caused by GM clearly warrants further studies.
The other potential metabolic pathways noted in our study were copper and retinol metabolisms that are less studied in PCa.Copper chaperone is a protein that ferries copper to cellular organelles, and interestingly, accumulation of copper has been reported in tumor cells, including PCa cells [30].Androgen receptor activation may enhance copper uptake, again suggesting that hormonal pathways would be involved in altered GM metabolism [31].In line   with our findings, higher retinol concentrations have been associated with an elevated PCa risk, and genetic variants in retinol pathways have previously been associated with PCa [32].
The main limitation of our study is the lack of sample size estimations and power calculations limiting analyses of minor microbial groups, but this would have been impossible to carry out without prior studies.Furthermore, low stool content in the sampling swabs, resulting in an unsatisfactory amount of bacterial DNA for 16S rRNA sequencing in 149 of 338 samples, could have been avoided by the fecal sampling method.In addition, patients with recent antibiotic use were not excluded, even though antimicrobials could affect GM.To account for this, use of antibiotics was one of the variables used to correct the analyses.One should also note that the study was limited to individuals with a relatively low-risk cancer suspicion, which may potentially dilute the results as cases with advanced or metastatic tumors were not included.The strengths of our study include the prospective design, detailed prospective data collection, and that, to our knowledge, this is the largest reported study on the subject.

Conclusions
In this study, we discovered differences in GM components between PCa patients and benign individuals.In a predictive analysis, microbial steroid hormone biosynthesis, mineral absorption, and retinol metabolism were potential carcinogenic pathways.Moreover, elevated predicted microbial 5-AR was associated with lower T levels in plasma.These findings could explain the previously observed association of lifestyle, geography, and PCa incidence.

Fig. 1 -
Fig.1-Study flowchart.A total of 364 patients were enrolled in the study, and after exclusion, 181 were included in the analyses.The available 169 blood samples were divided according to their use of 5-a-reductase inhibitors (5-ARI) into the main analysis group and 5-ARI users' group.
mass index; ISUP = International Society of Urological Pathology; PSA = prostate-specific antigen.a Kruskal-Wallis test.b Wilcoxon rank sum test.c Finasteride/dutasteride. d Pearson chi-square test.e Fisher's exact test.f High risk for antimicrobial resistance (Africa, Asia, and South America).E U R O P E A N U R O L O G Y O P E N S C I E N C E 6 2 ( 2 0 2 4 ) 1 4 0 -1 5 0

Fig. 2 -
Fig. 2 -(A) Bray-Curtis dissimilarity (b-diversity) of the gut microbiota between benign and prostate cancer (PCa) cases.Benign cases are highlighted with green and PCa cases with purple dots.The microbiota of benign and cancer patients differs significantly (PERMANOVA pseudo f-statistic 1.5, p = 0.039).(B) Abundance of Prevotella 9 in the principal coordinate (PCo) scatter plot 1 (10% of the calculated differences in diversity).Abundance of Prevotella 9 is also significantly elevated in PCa (differential abundance analysis Log2 fold change 1.5, FDR corrected p = 0.03).The color scale represents the abundance of Prevotella 9, varying from 4 (black) to 27 806 (blue).Upper bound on the color scale is Q3 (27 806) of the prostate cancer cases.FDR = false discovery rate; PERMANOVA = permutational multivariate analysis of variance.

Fig. 3 -
Fig. 3 -Relative abundances of the core microbiome level across the cancer grades at the family level.Bars indicate the percentage of the taxa.Color indicates the taxonomical phylum of the family: blue shades for Bacteroidetes, orange for Firmicutes, green for Proteobacteria, and gray for others.Prostate cancer cases were graded according to severity with ISUP grade groups.Family Prevotellaceae abundance elevates with cancer severity, but the elevation was not statistically significant (p = 0.86).ISUP = International Society of Urological Pathology.

Fig. 4 -
Fig. 4 -Statistically significant Log2 fold changes of microbial genera in prostate cancer versus benign.Genera more abundant in prostate cancer cases are shown with peach-orange-brown shaded bars, and genera lower in the cancer cases are shown with blue shaded bars.The shade of the color indicates the dominance of the genus by median abundance in the whole cohort.Genera are presented in taxonomical order.While Prevotella 9, Christensenellaceae R-7, and Escherichia-Shigella are abundant members of the gut microbiota, less abundant groups are also different.PCa = prostate cancer.
Alcohol dehydrogenase propanol preferring (5%) All-trans-retinol 13,14-reductase (20%) S-(hydroxymethyl) glutathione dehydrogenase/alcohol dehydrogenase (8%) CI = confidence interval; KEGG = Kyoto Encyclopedia of Genes and Genomes; PICRUSt = Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.E U R O P E A N U R O L O G Y O P E N S C I E N C E 6 2 ( 2 0 2 4 ) 1 4 0 -1 5 0 [16]Corp., Armonk, NY, USA) and R version 4.0.3.Continuous variables were summarized with medians and quartiles.For the main study questions, difference of medians and 95% confidence intervals (CIs) were calculated with quantile regression.PCa cases were grouped by two methods, each utilizing the International Society of Urological Pathology (ISUP) grade, namely, benign cases versus all cancer cases, and benign cases versus ISUP grade group 1, 2-3, and 4-5 cancers[16].Factors potentially impacting either cancer status or GM composition, such as age, body mass index, recent use of antibiotics, inflammatory bowel diseases (IBDs), recent high-risk travel history, and smoking, were also analyzed with Wilcoxon rank sum, Kruskal-Wallis, Pearson chi-square, or Fisher's exact tests.

Table 1 -
Clinicopathological characteristics of the participants

Table 2 -
PICRUSt-predicted statistically significant bacterial gene products associated with prostate cancer status